Unsupervised dimensionality reduction via principal component analysis 128
  Total and explained variance
  Feature transformation
  Principal component analysis in scikit-learn
Supervised data compression via linear discriminant analysis
  Computing the scatter matrices
  Selecting linear discriminants for the new feature subspace
  Projecting samples onto the new feature space
  LDA via scikit-learn
Using kernel principal component analysis for nonlinear mappings
  Kernel functions and the kernel trick
  Implementing a kernel principal component analysis in Python
    Example 1 – separating half-moon shapes
    Example 2 – separating concentric circles
  Projecting new data points
  Kernel principal component analysis in scikit-learn
Summary
